[R] generalized linear mixed models with a beta distribution

[Prof Brian Ripley]

glmmPQL can fit the same GLM families as glm() can -- it does not list 
_any_ .

Howver, the beta distribution does not give a GLM family and hence your 
subject line is strictly about a non-existent concept.  I'm presuming that 
you want to model the logit of the mean of a beta by a random effects 
model -- it is unclear what you want to do with the other parameter.

Note that the beta does fit into the framework of package gamlss, but I am 
not aware of an option for random effects in that framework.

On Wed, 12 Mar 2008, Craig A Faulhaber wrote:

> Greetings,
>
> I am interested in using a generalized linear mixed model with data that
> best fits a beta distribution (i.e., the data is bounded between 0 and 1
> but is not binomial).  I noticed that the beta distribution is not
> listed as an option in the "family objects" for glmmPQL or  lmer.  I
> found a thread on this listserve from 2006 ("[R] lmer and a response
> that is a proportion") that indicated that there was no package

https://stat.ethz.ch/pipermail/r-help/2006-December/121567.html

> available for mixed effects models with a beta distribution at that
> time.  This thread also indicated that package betareg did not allow
> inclusion of random effects.

But it did suggest modelling this in nlme via a variance specification, 
and that remains a good suggestion.

> Does anyone know of a package or code for a generalized linear mixed
> model that allows a beta distribution?  Transforming my data might allow
> me to use another family, but I would rather not transform the data if
> possible.  Thanks for your help!
>
> Sincerely,
> Craig Faulhaber

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
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